% With the distance kernel one can use any of the following distance metrics: 
% BrayCurtisDistance()
% CanberraMetric()
% CanberraWordDistance()
% ChebyshewMetric()
% ChiSquareDistance()
% CosineDistance()
% Distance()
% EuclidianDistance()
% GeodesicMetric()
% HammingWordDistance()
% JensenMetric()
% ManhattanMetric()
% ManhattanWordDistance()
% MinkowskiMetric()
% RealDistance()
% SimpleDistance()
% SparseDistance()
% SparseEuclidianDistance()
% StringDistance()
% TanimotoDistance()
% 

init_shogun

addpath('tools');
fm_train_real=load_matrix('../data/fm_train_real.dat');
fm_test_real=load_matrix('../data/fm_test_real.dat');
label_train_twoclass=load_matrix('../data/label_train_twoclass.dat');

% distance
disp('Distance')

feats_train=RealFeatures(fm_train_real);
feats_test=RealFeatures(fm_test_real);
width=1.7;
distance=EuclidianDistance();

kernel=DistanceKernel(feats_train, feats_test, width, distance);

km_train=kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
km_test=kernel.get_kernel_matrix();

